The combination of increasing global food demand with increased food security risks associated with climate change amid a decreasing number of skilled growers necessitates innovative solutions in green- house horticulture. Autonomous growing offers a solution based on greenhouse climate forecasting and (optimal) control. However, current theoretical models developed for greenhouse climate forecast- ing face limitations due to the in-depth physics knowledge required for their use and their dependence on intricate system parameters that are difficult to estimate. Conversely, machine learning models struggle with generalisation to unseen conditions and adherence to physical laws, leading to unrealis- tic predictions in greenhouse environments. This study addresses these challenges by exploring the use of Physics-Informed Machine Learning (PIML) techniques to enhance greenhouse climate fore- casting. A simple differentiable theoretical model for simulating the greenhouse climate is proposed to serve as prior physical knowledge of the system. Subsequently, a novel PIML model is introduced in the form of Controlled Aphynity (CA), which integrates insights from neural controlled differential equations and Aphynity, and is the first such model that allows for the augmentation of incomplete prior knowledge of a dynamical system with data-driven machine learning models while being adaptable to changing dynamics due to forces acting on the system. Experimental results show that CA is capable of successfully augmenting incorrect physics under changing dynamics on the task of humidity deficit prediction in the greenhouse. Furthermore, three ensemble methods combining CA with traditional ma- chine learning techniques are explored and demonstrate promising synergies. A detailed case study evaluates CA and the best-performing ensemble approach on the task of humidity deficit prediction under realistic greenhouse scenarios over a complete crop cycle. Both CA and the ensemble methods exhibit superior adherence to physical laws, lower data requirements, and improved performance on outliers compared to conventional machine learning methods. This research contributes to advancing greenhouse climate modelling, underscoring PIML’s potential in optimising the greenhouse climate.F